1. Technical Field of the Invention
The present invention relates, generally, to a computerized method for creating a CUSUM chart for data analysis as well as a unique method to evaluate trials in a manufacturing process that uses data historians.
More particularly, the present invention relates to a computerized method for creating a CUSUM chart, wherein: (a) one dependent variable CUSUM appears with one or more independent variables; (b) the method for determining which, if any, variables in a list take step changes on a pre-selected period of time used for measurement (e.g., one day or one hour) during the same pre-selected period of time that a dependent variable takes a step change; (c) the method overlaps a CUSUM chart for counting discrete events, such as breaks on a paper-machine against CUSUM charts of other process variables; or a continuous dependent variable such as overall efficiency and (d) the method mathematically determines when a step change has occurred on a CUSUM chart and then automatically identifies what other variable had the same step change date or time.
2. Description of the Prior Art
Industrial and manufacturing processes often employ a complex series of manufacturing steps, applied to one or more raw materials, to produce a product. A business goal is, generally, to maximize the process productivity by eliminating unscheduled downtime of the process, while maintaining a quality standard of the product. Machine processes are typically instrumented, employing a variety of sensors in communication with a computer system to measure and record various parameters relating to raw materials, the process itself, and the product. The data collected is useful to monitor equipment operation for consistency, and to track quality of both raw material and finished product. Additionally, faults that lead to costly production line downtime can be recorded.
Various methods have been employed to analyze data gathered relating to an industrial process for the purpose of identifying problem causation. Typically, known instrumentation and data recording systems gather a large amount of data, and then make the data available to commercial computer spreadsheet programs for treatment by standalone statistical analysis applications.
One particularly useful method for analysis of problem causation in industrial processes, such as, for example, in the chemical industry, where many suppliers sell chemicals and other consumables and often run trials to assess how well a chemical or consumable has performed, is a use of a CUSUM chart. A CUSUM chart is a type of control chart and “CUSUM” stands for “cumulative sum control chart. CUSUM charts used to detect small changes between 0-0.5 sigma. For larger shifts (0.5-2.5), Shewart-type charts are generally used. CUSUM charts plot the cumulative sum of the deviations between each data point (a sample average) and a reference value, historically designated by “T.”
Unlike other control charts, one studying a CUSUM chart will be concerned with the slope of the plotted line, not just the distance between plotted points and the centerline. Critical limits for a CUSUM chart are not fixed or parallel and a mask in the shape of a “V” is usually laid over the chart with the origin over the last plotted point. Previous points covered by the mask indicate the process has shifted.
A CUSUM chart is, therefore, a proven statistical method to detect shifts in process data and created by entering data into an application after it is filtered for operational constraints and then calculating the numbers used to create the chart.
Historically, creating a CUSUM chart is simple:                Calculate the average of the variable of interest; call this “AVG”;        Take the first observation and subtract the average (AVG). Call this “A”; and,        Next, take the second observation and subtract the average (AVG) and to this add “A”. Repeat this calculation for each variable until you reach the last one. The value of “A” will now be 0.        
An example of calculations for developing a CUSUM chart, as known to the prior art, is seen in FIG. 8. An example of a CUSUM chart is seen in FIG. 9. The CUSUM chart of hole counts on a paper machine (FIG. 9), which graphically illustrates that hole counts peaked on Oct. 27, 2005, and have since been below average.
The statistical techniques used by the prior art to create CUSUM charts are not based on parametric statistics, thereby requiring certain assumptions to be made initially to produce usable results. Such assumptions are often ignored, leading to compromised results. Most tools using a parametric method employ regression-based analysis, often leading to a problem of causation versus correlation.
In the paper industry, in particular, there is no rational method for counting sheet breaks on a paper machine using historical data based on criteria such as break duration, grade or product, time since the last break, etc.
Further, there is a need to be able to count breaks on a paper machine ex-poste facto based on certain criteria, creating a CUSUM chart, and then determining what other variables changed at the same time. There is also a need to analyze the mathematics behind a CUSUM chart in a variety of industrial processes without necessarily forcing a user to examine charts. For example, one can calculate the mathematical minimum of a CUSUM chart (the point at which the cumulative average was the lowest) and then mine data from data historians to automatically determine what other variables had either a minimum or maximum CUSUM value at the same time.
Consequently, there is a need for an automated process for mining data accumulated from a computerized data historian that monitors a manufacturing process, and analyzes the data to determine factors in the manufacturing process that need to be altered in order to improve the efficiency of the manufacturing process. Thus, a computerized method for creating and analyzing CUSUM charts is desired.